Failures In Induction Motors Research Articles

Evaluation of electromagnetic performance of emerging failures in electrical machines using computational simulation

Common failures in industrial induction motors equipped with squirrel cage rotor are examined in this paper. These failures are: broken rotor bars and short circuit in the inter-turn winding. The broken rotor bars failure was simulated with two consecutive broken bars in order to see how the magnetic flux density is affected. The inter-turn short circuit was simulated with 40% reduction of winding coil in an inter-turn short circuit, for one of the phases. The waveform of the flux density was plotted directly, while the analysis of the harmonic distribution and its relation to the electrical failures was performed in the Fourier domain. The proposed methodology allows to clearly identify the fault conditions analyzed. Comparative test of the real failure was performed in order to verify the results obtained from the simulation; good agreement between simulation and experimental measurements was obtained.

Fault Diagnosis of Low-Power Three-Phase Induction Motor

Abstract An Induction motor reliability survey at an egg processing plant shows that almost 50% of the total motor failures are fan induction motors. Visual investigations of the faulty fan motors show that the main cause of the induction motor failure is air gap eccentricity. In this study, experimental tests are performed on a 1.1kW three-phase induction motor to detect air gap eccentricity and overheating of the induction motor. Heating tests show that end shield housing temperature reaches 100°C with blocked air flow from the fan, which can reduce the lifespan of the bearing. Dimension measurements of the end shield housing show that the dimensions of both tested motors back-end shields are larger than ISO tolerance grade limit. It leads to a loose fit between the housing and bearing, causing air gap eccentricity. Also, both motor back end shield housing has an out-of-round condition leading to an unbalanced magnetic pull. To detect the air gap eccentricity caused by too loose of a fit between housing bore and bearing, current Park’s vector approach is used. To measure three phase current, Hall Effect current transducers, a digital oscilloscope is used and Matlab software to process the measurement data. Results show that Park’s vector approach can be used to detect the air gap eccentricity caused by too loose a fit between bearing and housing. Therefore, the Park’s vector approach can be used to diagnose air gap eccentricity and analyse the type of the air gap eccentricity.

Evaluation of Electromagnetic Performance of Emerging Failures in Electrical Machines

In the present paper, electromagnetic simulations of electric motors with early failures are presented. One of the most common failures in induction motors with squirrel cage rotor is considered in this paper. These failures are: broken rotor bars and short circuit in the inter-turn winding. The broken rotor bars failure was simulated with two consecutive broken bars in order to see how the magnetic flux density is affected. The inter-turn short circuit was simulated with the 40% reduction of winding coil in an inter-turn short circuit for one of the phases. In both cases the simulation was performed with the finite element software FEMM and the waveform of the flux density in both the motor air gap and the magnetic core was calculated. Comparative test of the real failure was performed in order to verify the results obtained from the simulation, good agreement between simulation and experimental measurements was obtained.

An availability of MEMS-based accelerometers and current sensors in machinery fault diagnosis

Abstract In recent years, micro-electromechanical systems (MEMS)-based sensors have shown huge attraction in machinery fault diagnosis due to their low power consumption, low cost, small size, mobility, and flexibility. Hence, this paper presents a comprehensive fault diagnosis scheme using MEMS-based accelerometers and current sensors to identify several induction motor failures. In this paper, we first verify the reliability of these MEMS-based sensors via frequency analysis for vibration and current signals captured by them. Likewise, this paper validates their suitability for machinery fault diagnosis. To do this, we configure a 147-dimensional feature vector using statistical values (i.e., 21 statistical values × 7 MEMS-based accelerometers and current sensors), analyze fault signatures by employing a kernel principal component analysis, and pinpoint types of induction motor failures with one-against-all multi-class support vector machines (OAA MCSVMs), a random forest (RF), and a fuzzy k-nearest neighbor (Fk-NN). Experimental results indicate that the presented fault diagnosis approach using MEMS-based accelerometers and current sensors yields 100%, 86%, and 80% of classification accuracy with OAA MCSVMs, the RF, and the Fk-NN, respectively. Accordingly, MEMS-based sensors are enough for substituting commercial accelerometers and current sensors that are used for fault diagnosis. Specifically, MEMS-based accelerometers are far more effective for preserving intrinsic information about various induction motor failures than MEMS-based current sensors, offering at least 38% performance improvement in classification accuracy.

Premature Wear and Recurring Bearing Failures in an Inverter-Driven Induction Motor—Part I: Investigation of the Problem

This paper provides an analysis about early incipient and recurring failures in three-phase induction motor bearings when driven by pulsewidth modulation (PWM) inverters, focusing on a real industrial process. The potential problems preliminarily defined were first discussed on a theoretical basis and afterward experimentally assessed by means of vibration, stator current, and common-mode current data acquisition and analysis. Over the investigation, it was concluded that the presence of common-mode currents at the verified levels could cause damages to the motor bearings, which was confirmed when the machine stopped working due to another bearing failure.

Detecting Bearing Faults in Line-Connected Induction Motors Using Information Theory Measures and Neural Networks

Fault detection in electrical machines is a topic widely explored by researchers, especially bearing faults that represent about half of the total three-phase induction motor failure occurrences. This kind of fault is detectable by specific frequencies of the stator current and is a wide source of investigation. Thus, this work presents a predictability analysis method that provides patterns based on measures of relative entropy, Bhattacharyya distance, and Lempel–Ziv complexity estimated over reconstructed signals obtained from wavelet packet decomposition components. The signals under study were collected from motors with faults in the inner or outer races, which were artificially created in laboratory. These patterns were applied to three neural network topologies, which were used to classify the signals into two groups: normal or faulty.

Combination of Noninvasive Approaches for General Assessment of Induction Motors

There exists no single quantity able to diagnose all possible failures taking place in induction motors. Currents and vibrations monitoring are rather common in the industry, but each of these quantities alone can only detect some specific failures. Moreover, even for the specific faults that a quantity is supposed to detect, many problems may rise. As a consequence, a reliable and general diagnosis system cannot rely on a single quantity. On the other hand, it would be desirable to rely on quantities that can be measured in a noninvasive way, which is a crucial requirement in many industrial applications. This paper proposes a twofold method to detect electromechanical failures in induction motors. The method relies on analysis of currents (steady state + transient) combined with analysis of infrared data captured by using appropriate cameras. Each of these noninvasive techniques may provide complementary information that may be very useful to diagnose an enough wide range of failures. In the present paper, the detection of three illustrative faults is analyzed: broken rotor bars, cooling system problems and bearing failures. The results show the potential of the methodology that may be particularly suitable for large, expensive motors, where the prevention of eventual failures justifies the costs of such system, due to the catastrophic implications that these unexpected faults may have.

A New Methodology for Complementary Diagnosis of Induction Motors Based on Infrared Thermography

The importance of the induction motors in the industrial context is doubtless because they are involved in a wide variety of applications. This extensive use justifies the importance of developing reliable techniques to assess the condition of these machines. In this context the main objective of the present paper is to propose a reliable and accurate predictive methodology to diagnose failures in induction motors based on infrared thermography and relying onthe principles of the First Law of Thermodynamics. To do so, experimental tests in 1.1kW induction motors were performed under healthy and faulty conditions. In all cases, a continuous monitoring of the key conditions was performed and infrared thermography images were taken. The results show the high potential the model has for general diagnoses purposes due to its high accuracy in predicting the heating behavior of the motor in healthy and faulty conditions. Additionally, the registration of the infrared images during the motor heating process has proven to be an excellent tool to diagnose several failures and the average temperature profiles obtained at different regions of the motor revealing interesting patterns that can be used to identify the fault.

Fused Empirical Mode Decomposition and MUSIC Algorithms for Detecting Multiple Combined Faults in Induction Motors

Detection of failures in induction motors is one of the most important concerns in industry. An unexpected fault in the induction motors can cause a loss of financial resources and waste of time that most companies cannot afford. The contribution of this paper is a fusion of the Empirical Mode Decomposition (EMD) and Multiple Signal Classification (MUSIC) methodologies for detection of multiple combined faults which provides an accurate and effective strategy for the motor condition diagnosis.

Premature Wear and Recurring Bearing Failures in an Inverter-Driven Induction Motor—Part II: The Proposed Solution

In a previous work, a real case of recurring bearing failures in an induction motor had been extensively studied. As a result, the cause of the problem was revealed to be the circulation of electric current through the bearings. In this way, the present paper deals with the high-frequency leakage currents in ac motors fed by pulsewidth-modulation inverters, contextualized in the analysis of an actual case. From the parameters of an equivalent circuit representing the connecting cable, motor, and inverter return path, a filter, characterized by a common-mode transformer, is designed to mitigate the common-mode quantities and the correlated problems. Both simulated and experimental results show that the proposed solution represents an advantageous alternative in comparison to the traditional $RC$ and $RLC$ filters, since, in addition to its effect on the common-mode quantities, it dissipates a negligible amount of power.

Computational Modeling of Common Failures in Electrical Induction Machines

One of the most common types of failures in induction motors with squirrel cage rotor is examined in this paper. Generally, such failure is characterized by: broken rotor bars and short circuit in the inter-turn winding. The broken rotor bars failure was simulated with two consecutive broken bars in order to see how the magnetic flux density within be affected. The inter-turn short circuit was simulated with 40% reduction of winding coil in an inter-turn short circuit for one of the phases. In both cases the simulation was performed with the finite element software FEMM© , which helped in obtaining the waveform of the flux density in the motor airgap and the magnetic core. The result obtained allows clear identification of the type of failures that occurs depending of type that will present in the simulation.

Virtual Instrumentation Based Analysis of Induction Motor

The three phase induction motor is a popularly used machine in many of the industries, which is well known for its robustness, reliability, cost effectiveness, efficient and safe operation. The unnoticed manufacturing failure, mistakes during repair work, exceeding life time may be some of the causes of the induction motor failure, which may lead to the unknown shut down time of the industry. The condition monitoring plays important role as it has the influence on the production of materials and profit. In our work, the induction motor is modelled using stationary reference frame and analysed for single phasing stator fault. The techniques used in detecting the single phasing (open circuit) failures are Park’s vector approach and Fast Fourier Transform (FFT). Park’s vector approach is used for detecting the faults occurring at various phases and FFT is used for detecting the faults of the induction motor working under no load and varying loading conditions.

A combined technique for rotor broken bar diagnosis in VSD equipped with vibration reducers

Broken rotor bar is a common fault in squirrel cage induction motors. Rotor failures due to that fault now account for a moderate percentage of total induction motor failures. This paper will deal with detection of broken rotor bar of induction motor supplied from variable speed drive (VSD) equipped with AC reactor and sine-wave shaping filter on the output side. The experimental results show that the instantaneous power spectrum analysis method is only effective when the motor is supplied from sinusoidal supply, in contrast for the demonstrated VSD. The instantaneous power spectrums of the healthy rotor case and the broken bar rotor cases is processed with mean absolute difference (MAD) approach algorithm to investigate the dissimilarity between them. The results show that this combined technique is highly effective in the diagnosis of broken rotor bar fault in case of the demonstrated VSD.

Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments.

Fault Diagnosis of the Asynchronous Machines Through Magnetic Signature Analysis Using Finite-Element Method and Neural Networks

This paper presents a method for the identification of winding failures in induction motors. The types of failures include unbalanced currents flowing into the motor and short-circuit of the winding. The radiated magnetic field of a typical induction motor was studied while various types of failures applied to the machine. The implementation was performed by applying different types of unbalanced currents flow into the machine. The fields were obtained from both numerical finite-element simulations as well as from experimental setups. The turn to terminal and turn to turn short-circuit of the motor's winding were studied. The frequency response of the 3-D finite-element (3DFE) model of the motor was implemented up to high-order frequencies. The numerical results were compared with the measurement results. The fields with unbalanced currents and short-circuit conditions were identified by studying the harmonic orders of the radiated magnetic fields. This was also implemented using artificial neural networks (ANN). The results show that the signature study of the experimental as well as the simulation models can be utilized for failure identification in electric motors with a high level of accuracy.

A Novel Approach for Broken Bar Fault Diagnosis in Induction Motors Through Torque Monitoring

The cracked or broken bar fault constitutes about 5-10% of total induction motor failures and leads to malfunction as well as reduction of the motor's life cycle. This is the reason why there is continuous research on techniques for prompt detection. In this study, a study of the influences of the broken bar fault to the electromagnetic characteristics of the induction motor is presented, using an asynchronous cage motor and finite element method analysis. To this direction, two models have been created and studied: a healthy and one with a broken bar. Additionally, a new approach on the detection of the broken rotor bar fault through the electromagnetic torque monitoring is suggested and validated through experimental results.

A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals

Failure of induction motors are a large concern due to its influence over industrial production. Motor current signature analysis (MCSA) is common practice in industry to find motor faults. This paper presents a new approach to detection and diagnosis of motor bearing faults based on induction motor stator current analysis. Tests were performed with three bearing conditions: baseline, outer race fault and inner race fault. Because the signals associated with faults produce small modulations to supply component and high nose levels, a modulation signal bispectrum (MSB) is used in this paper to detect and diagnose different motor bearing defects. The results show that bearing faults can induced a detestable amplitude increases at its characteristic frequencies. MSB peaks show a clear difference at these frequencies whereas conventional power spectrum provides change evidences only at some of the frequencies. This shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for fault detection and diagnosis. In addition, the study also show that current signals from motors with variable frequency drive controller have too much noise and it is unlikely to discriminate the small bearing fault component.

Root Cause Methodology for Induction Motors: A Step-by-Step Guide to Examining Failure

Over the course of my 40-year career in the motor industry, I have been involved in an extensive taxonomic study and ongoing research into squirrel cage induction motor failure analysis. This topic is particularly relevant now as the petrochemical industry is moving toward reliability-based maintenance and has begun to require increased attention to prevent repeated failures. This article is intended to abridge and update numerous previously published articles on the subject and present a root cause failure methodology that will assist in the petrochemical industry's transition. The scope of the project includes squirrel cage induction motors up to 3,000 hp; however, much of this methodology includes applications to larger sizes and types.

Power-Integrated Circuit Active Leakage Current Detector

Most of the failures of induction motors become insulation faults, causing a permanent damage. Using differential current transformers, a system capable of insulation fault detection was developed, based on the differential relay protection scheme. Both signal injection and fault detection circuitry were integrated in a single chip. The proposed scheme is faster than other existing protection and not restricted to protect induction motors, but several other devices (such as IGBTs) and systems. This paper explains the principle of operation of fault protection scheme and analyzes an integrated implementation through simulations and experimental results. A power-integrated circuit (PIC) implementation is presented.

A Comparison of Different Techniques for Induction Motor Rotor Fault Diagnosis

The problem of failures in induction motors is a large concern due to its significant influence over industrial production. Therefore a large number of detection techniques were presented to avoid this problem. This paper presents the comparison results of induction motor rotor fault detection using three methods: motor current signature analysis (MCSA), surface vibration (SV), and instantaneous angular speed (IAS). These three measurements were performed under different loads with three rotor conditions: baseline, one rotor bar broken and two rotor bar broken. The faults can be detected and diagnosed based on the amplitude difference of the characteristic frequency components of power spectrum. However IAS may be the best technique because it gives the clearest spectrum representation in which the largest amplitude change is observed due to the faults.